Systems and methods for automatic detection and quantification of pathology using dynamic feature classification

ABSTRACT

Methods, devices, and systems are provided for quantifying an extent of various pathology patterns in scanned subject images. The detection and quantification of pathology is performed automatically and unsupervised via a trained system. The methods, devices, and systems described herein generate unique dictionaries of elements based on actual image data scans to automatically identify pathology of new image data scans of subjects. The automatic detection and quantification system can detect a number of pathologies including a usual interstitial pneumonia pattern on computed tomography images, which is subject to high inter-observer variation, in the diagnosis of idiopathic pulmonary fibrosis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. §119(e)to U.S. Provisional Patent Application No. 62/335,816, filed May 13,2016. The entire disclosure of U.S. Provisional Patent Application No.62/335,816 is incorporated herein by reference.

FIELD

The present disclosure is generally directed to computer diagnosticsystems and more specifically to systems for automatically detecting andquantifying pathology in scanned images.

BACKGROUND

In general, certain pathologies manifest as abnormalities in the tissuesof a subject or patient. While these abnormalities may not be detectableduring a routine exam, symptoms of the pathology may cause a diagnosingprofessional to recommend and/or administer various imaging scans of asubject to obtain images of suspect tissues/areas. Existing systems forreviewing imaging scans rely on human interaction and are prone tomisclassification and misidentification of pathologies. While diagnosingand treating irregularities in patients is often time-critical andurgent, the time required to confidently identify and/or classify thepresence, location and/or extent of such irregularities and normaltissues through contemporary methods is typically great, often at therisk of a patient's health.

Besides consuming time at a time-critical moment, contemporary methodsof identifying and/or classifying the presence, location and/or extentof such irregularities and normal tissues require great expertise.Typically today's imagers are required to be licensed and boardcertified and complete medical school training and years of postgraduatetraining in the form of a residency. Even still, misidentificationand/or misclassification occurs often and second or third opinions maybe required. As a result, experts capable of reviewing imaging scans forirregularities are rarely readily accessible, causing more of a timedelay, are not always correct, and may increase medical expenses.

Also, while such imaging scans are used around the world to classify andidentify irregularities, interaction and collaboration between doctorsis either rare or impossible in many circumstances. In fact, somepathologies maybe undetectable by the human eye. For example, while adoctor in one country may have a special ability to identify aparticular irregularity, another doctor in another country may lack suchan ability and cannot access such information. As a result, today'smethods of reviewing imaging scans and identifying and/or classifyingirregularities has resulted in a wealth of information that cannoteasily be shared.

Furthermore, many patients around the world have limited access tohealthcare and particularly lack access to such experts as would be ableto identify rare diseases by reviewing medical scans throughcontemporary methods. As the cost of machines capable of generatingimaging scans decreases through advancing technology, the worldpopulation is increasing and thus the ratio of patients to doctorsthroughout the world is growing at an increasing rate. Thus methods andsystems of identifying such irregularities without the requirement of atrained medical expert is greatly needed.

Finally, in some cases, a diagnosing professional may review whether apathology has changed over time. In any event, this assessment of changeis susceptible to interpretation error, inter-observer variation,variations in scan quality, and is generally subjective in nature. Assuch, proper patient care using contemporary techniques may requireroutine reviews of medical scans. Accordingly, today's imaging expertsmay be incapable of personally reviewing each and every scan of everypotential irregularity throughout the world in the short time that apatient may need such a review.

SUMMARY

It is with respect to the above issues and other problems that theembodiments presented herein were contemplated. What is needed is atime-efficient, accurate, fast-learning, method of identifying and/orclassifying irregularities in imaging scans which does not require anexpert in pathology and is capable of detecting irregularities which maynot be detectable by even an expertly trained human eye. In general,embodiments of the present disclosure provide methods, devices, andsystems for quantifying an extent of various pathology patterns inscanned subject images. Scanned images may include images received fromany imaging modality. For instance, imaging modalities may include, butare in no way limited to, computed tomography (CT), magnetic resonanceimaging (MM), optical microscopy, or other biomedical imaging modalitycapable of producing image data. In one embodiment, the diagnosis andquantification of pathology may be performed automatically andunsupervised via a computer system. For instance, the methods, devices,and systems described herein may be used to automatically identify ausual interstitial pneumonia (UIP) pattern on computed tomography (CT)images of the lungs, which is subject to high inter-observer variation,in the diagnosis of idiopathic pulmonary fibrosis (IPF).

While described in conjunction with identifying and quantifying extentof UIP, or lung fibrosis, on CT images, it should be appreciated thatthe various embodiments described herein are not so limited. Forexample, the methods, devices, and systems disclosed herein may be usedto identify and/or quantify any pathology capable of producingdistinguishing patterns in local regions of scanned image data (e.g.,where scanned image data is different, or distinguishable, betweenhealthy tissue and pathological tissue, etc.). Moreover, embodiments ofthe present disclosure are not limited to the lungs of a subject and mayapply to any region, area, site, or portion of a subject capable ofproducing distinguishing scanned image data.

In some embodiments, the quantification of imaging abnormalitiesincludes employing a set of features or basis elements, or a dictionaryof elements, representing features of relatively small image regionsand/or features of image regions at multiple scales depicting normaltissue and/or pathology. Once generated, the dictionary may be used toencode other image regions. In some embodiments, the dictionary can begenerated by receiving image data scans of a particular area with and/orwithout pathology taken from a number of subjects. Local regions, orblocks, may be extracted from these image data and an initial analysissuch as clustering applied to determine, or “learn,” a number of featurerepresentations. The sizes, configurations and dimensionality of blocksmay vary depending on application. In one embodiment, the resultingdictionary may include elements that represent low-level features suchas directed edges or blobs. In any event, other novel image regions, notincluded in the dictionary construction process, may be reconstructedmathematically using weighted combinations of dictionary elements.Various methods may be used to determine weighting coefficients, whichprovide descriptive quantitative features useful in subsequentclassification analysis. Pooling weighting coefficients over imageregions of a larger scale than dictionary elements, for example byaveraging weighting coefficients computed within a region several timesthe size of dictionary elements, produces distinctive quantitativesignatures.

Technical improvements provided by the disclosure may relate to thecomplexity of the “dictionary elements” and how they are computed. Incertain embodiments, hierarchical features, i.e. more complicateddictionary elements may be used where higher level elements are computedin terms of lower level dictionary elements. These hierarchical featuresmay take into account more image context (not just one small slidingwindow) by concatenating multiple layers of dictionary features atdifferent image scales.

In some embodiments, clustering operations (e.g. k-means) may be used todevelop the feature dictionary. In some embodiments, mathematicaloptimization may be used to develop and/or fine tune or refinedictionary elements to maximize classification accuracy, e.g. update apreviously computed dictionary so the best elements for a givenclassification task are included. In this context the dictionaryelements could be referred to as “convolutional filters”.

In some embodiments, a classification model utilizing features containedin a dictionary may be trained in a supervised fashion using expertlabeled examples. In some embodiments, the process may compriseclassifying image regions into one of a number of categories defined byexpert labeled exemplars. In a lung classification application this mayproduce a composite score for a subject. For example, 80% of sampledimage regions classified as normal lung, 12% classified as reticularabnormality, 5% classified as honeycombing and 3% not classified. Insome embodiments, a classification model may be trained in anunsupervised fashion not reliant on expert labeled image regions fortraining. In some embodiments, an additional clustering process may beused to classify tissue types instead of supervised training usingexpert labeled images. With this additional clustering classificationcategories may not have semantic labels or expert assigned names such asnormal, reticular abnormality or honeycombing, in which case a compositeclassification score may be 3% category 1, 0.8% category 2, . . . 1%category N wherein the sum over all N categories is 100%.

By way of example, a number of image data scans (e.g., generated bycomputed tomography, etc.) may be taken of the lungs of subjects withand without pathology (e.g., having IPF and not having IPF, etc.). Theimage data scans may include multiple scans taken from each subject.Utilizing a fixed block size (e.g., 3 mm×3 mm, etc.), a computer systemmay automatically extract image blocks from within a defined area of theimage data scan. This analysis may include moving along a path withinthe defined area and extracting image blocks of fixed size that areadjacent to one another. Additionally or alternatively, the analysis mayinclude extracting blocks of varying size from one or more image blocksthat are overlapping one another within the defined area. It should beappreciated, that the block size may correspond to a defined pixel area.In any event, a clustering process (e.g., a modified k-means clusteringprocess, etc.) or optimization process may be applied to the pixelintensities in each of the extracted blocks to produce a dictionary ofelements (e.g., an element dictionary of image data scan patterns foundin the lungs, etc.). Various mathematical pre-processing methods (e.g.data whitening and/or contrast normalization) may be applied prior toclustering depending on the application. As pixel intensities orluminance may vary across a length and/or a width of an extracted block,each element in the dictionary may be generated to uniquely describe orcharacterize a scanned feature or a portion thereof defined by thevariation in pixel intensity and/or luminance over distance. In someembodiments, pixel intensities may be preferred over luminance values.For example, in CT images, the brightness of pixels is proportional tomaterial density and CT pixel values are usually expressed on anestablished scale (called the Hounsfield Unit (HU) scale). Larger imageregions (e.g. 14×14 mm) can be described by pooling (e.g. by average ormaximum values) dictionary element weighting coefficient values for eachoverlapping block within the region.

Once the dictionary is generated, an automatic detection andquantification system may be constructed to classify image regionsdemonstrating normal and abnormal tissue in other subjects. Theclassification system may be based on supervised training where anexpert (e.g., experienced radiologist, etc.) delineates and labelsregions of interest (ROIs) in representative image data scans thatdemonstrate the characteristic patterns of a specific pathology (e.g.,UIP, etc.). In the example above, characteristics of UIP includepatterns generally known as honeycombing, reticular abnormality, andtraction bronchiectasis. This delineation may be performed by outliningor otherwise identifying regions in the image data scans. In someembodiments, the delineated regions may be verified by another expert(e.g., another experienced radiologist, a senior thoracic radiologist,etc.) before using the delineations in training the automatic detectionand quantification system. Utilizing the previously constructeddictionary, quantitative features may be computed for each of the ROIs.

Next, the ROIs and associated quantitative features, or labeledexemplars, are presented to a classifier algorithm for training. Avariety of classifier algorithms including artificial neural network(ANN), Support Vector Machine (SVM), Logistic Regression (LR) or RandomForest (RF) may be used separately or as an ensemble. In someembodiments, the labeled exemplars may be used to form training examplesfor the classifier algorithm. In one embodiment, each of the trainingexamples is marked as being associated with a particular characteristicpattern of a specific pathology, or classification category. In responseto receiving the training examples, the classifier algorithm maygenerate a predictive model that is configured to classify new imagescan data as being associated with one or more of the characteristicpatterns of specific pathology, or categories. In some embodiments, theclassifier algorithm may be trained to identify whether regions of a newimage data scan are associated with normal or pathological tissue (e.g.,normal or fibrotic lung, etc.). These regions may be automaticallyclassified and/or marked by the classifier algorithm for each scan. Inone embodiment, only tissue having pathology is marked or otherwiseidentified by the classifier algorithm. In any event, the classifieralgorithm may analyze a new image data scan and automatically determineareas of tissue having pathology according to any classificationcategory. A total quantitative score may be generated, for example bycomputing fraction of regions classified as abnormal.

As can be appreciated, the automatic detection and quantification systemdescribed herein may quickly and accurately determine a particularcharacteristic pattern of a specific pathology in a subject usingquantifiable data evaluation techniques. Unlike human interpretation, orqualitative analysis, the methods, devices, and systems as describedherein are in no way affected by user error, user bias, orinter-observer variation. In addition, the automatic detection andquantification system is capable of performing an analysis on tens,hundreds, or even thousands of image data scans in fractions of asecond. At least one benefit to the system described herein is that eachimage data scan associated with subject, which may amount to hundreds oreven thousands of scans per subject, can be quickly and accuratelychecked to ensure that any existent or detectable pathology is found. Asdisclosed herein, the system may be used on two-dimensional (2D) imagesections, three-dimensional (3D) image volumes, and/or othermulti-dimensional image representations.

The term “computer-readable medium,” as used herein, refers to anytangible data storage medium that participates in providing instructionsto a processor for execution. Such a medium may take many forms,including but not limited to, non-volatile media, volatile media, andtransmission media. Non-volatile media includes, for example, NVRAM, ormagnetic or optical disks. Volatile media includes dynamic memory, suchas main memory. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, orany other magnetic medium, magneto-optical medium, a CD-ROM, any otheroptical medium, punch cards, paper tape, any other physical medium withpatterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solidstate medium like a memory card, any other memory chip or cartridge, orany other medium from which a computer can read instructions. When thecomputer-readable medium is configured as part of a database, it is tobe understood that the database may be any type of database, such asrelational, hierarchical, object-oriented, and/or the like. Accordingly,the disclosure is considered to include a tangible storage medium ordistribution medium and prior art-recognized equivalents and successormedia, in which the software implementations of the present disclosureare stored.

The phrases “at least one”, “one or more”, and “and/or” are open-endedexpressions that are both conjunctive and disjunctive in operation. Forexample, each of the expressions “at least one of A, B and C”, “at leastone of A, B, or C”, “one or more of A, B, and C”, “one or more of A, B,or C” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.When each one of A, B, and C in the above expressions refers to anelement, such as X, Y, and Z, or class of elements, such as X₁-X_(n),Y₁-Y_(m), and Z₁-Z_(o), the phrase is intended to refer to a singleelement selected from X, Y, and Z, a combination of elements selectedfrom the same class (e.g., X₁ and X₂) as well as a combination ofelements selected from two or more classes (e.g., Y₁ and Z_(o)).

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising”, “including”, and “having” can be used interchangeably.

The terms “determine,” “calculate,” and “compute,” and variationsthereof, as used herein, are used interchangeably and include any typeof methodology, process, mathematical operation, or technique.

The term “means” as used herein shall be given its broadest possibleinterpretation in accordance with 35 U.S.C., Section 112, Paragraph 6.Accordingly, a claim incorporating the term “means” shall cover allstructures, materials, or acts set forth herein, and all of theequivalents thereof. Further, the structures, materials or acts and theequivalents thereof shall include all those described in the summary ofthe invention, brief description of the drawings, detailed description,abstract, and claims themselves.

The term “module” as used herein refers to any known or later developedhardware, software, firmware, artificial intelligence, fuzzy logic, orcombination of hardware and software that is capable of performing thefunctionality associated with that element.

It should be understood that every maximum numerical limitation giventhroughout this disclosure is deemed to include each and every lowernumerical limitation as an alternative, as if such lower numericallimitations were expressly written herein. Every minimum numericallimitation given throughout this disclosure is deemed to include eachand every higher numerical limitation as an alternative, as if suchhigher numerical limitations were expressly written herein. Everynumerical range given throughout this disclosure is deemed to includeeach and every narrower numerical range that falls within such broadernumerical range, as if such narrower numerical ranges were all expresslywritten herein.

The preceding is a simplified summary of the disclosure to provide anunderstanding of some aspects of the disclosure. This summary is neitheran extensive nor exhaustive overview of the disclosure and its variousaspects, embodiments, and configurations. It is intended neither toidentify key or critical elements of the disclosure nor to delineate thescope of the disclosure but to present selected concepts of thedisclosure in a simplified form as an introduction to the more detaileddescription presented below. As will be appreciated, other aspects,embodiments, and configurations of the disclosure are possibleutilizing, alone or in combination, one or more of the features setforth above or described in detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are incorporated into and form a part of thespecification to illustrate several examples of the present disclosure.These drawings, together with the description, explain the principles ofthe disclosure. The drawings simply illustrate preferred and alternativeexamples of how the disclosure can be made and used and are not to beconstrued as limiting the disclosure to only the illustrated anddescribed examples. Further features and advantages will become apparentfrom the following, more detailed, description of the various aspects,embodiments, and configurations of the disclosure, as illustrated by thedrawings referenced below.

FIG. 1 is a diagram depicting an automatic detection and quantificationsystem in a communications environment in accordance with embodiments ofthe present disclosure;

FIG. 2 is a block diagram depicting an automatic detection andquantification system in accordance with embodiments of the presentdisclosure;

FIG. 3 is a block diagram depicting a method of training an automaticdetection and quantification system and utilizing the trained automaticdetection and quantification system in accordance with embodiments ofthe present disclosure;

FIG. 4 shows a detail view of an image block of a subject image datascan in accordance with embodiments of the present disclosure;

FIG. 5 is a diagram illustrating a set of dictionary elements generatedfrom a number of image data scans in accordance with embodiments of thepresent disclosure;

FIG. 6 is a diagram illustrating quantification of a subject regionusing the set of dictionary elements generated in accordance withembodiments of the present disclosure;

FIG. 7 shows a chart of image data scans grouped by identified pathologytypes in accordance with embodiments of the present disclosure;

FIGS. 8A-8B show image blocks taken from image data scans used in vectorquantization in accordance with embodiments of the present disclosure;

FIG. 9A is a diagram of an image data scan including human identifiedregions of interest;

FIG. 9B is a diagram of an image data scan including identified regionsof interest by the automatic detection and quantification system inaccordance with embodiments of the present disclosure;

FIG. 10A shows a sample image data scan of a subject in accordance withembodiments of the present disclosure;

FIG. 10B shows the image data scan of FIG. 10A including regions ofinterest identified by the automatic detection and quantification systemin accordance with embodiments of the present disclosure;

FIG. 11 is a flow chart depicting a method of generating a dictionary ofelements in accordance with embodiments of the present disclosure;

FIG. 12 is a flow chart depicting a method of training an automaticdetection and quantification system in accordance with embodiments ofthe present disclosure; and

FIG. 13 is a flow chart depicting a method of identifying pathology inthe automatic detection and quantification of pathology in accordancewith embodiments of the present disclosure.

DETAILED DESCRIPTION

Before any embodiments of the disclosure are explained in detail, it isto be understood that the disclosure is not limited in its applicationto the details of construction and the arrangement of components setforth in the following description or illustrated in the followingdrawings. The disclosure is capable of other embodiments and of beingpracticed or of being carried out in various ways. Also, it is to beunderstood that the phraseology and terminology used herein is for thepurpose of description and should not be regarded as limiting. The useof “including,” “comprising,” or “having” and variations thereof hereinis meant to encompass the items listed thereafter and equivalentsthereof as well as additional items.

FIG. 1 is a diagram depicting an automatic detection and quantificationsystem in a communications environment 100 in accordance withembodiments of the present disclosure. The environment 100 includes anautomatic detection and quantification (ADAQ) system 104 with one ormore elements dictionaries 108. In one embodiment, the ADAQ system 104may be referred to as a data-driven textural analysis (DTA) system. Inany event, the ADAQ system 104 may be configured to receive scannedimage data across a communication network 112. This scanned image datamay be stored in a memory 118 after an image scan system 116 scans asubject 106. In some embodiments, the ADAQ system 104 may communicatewith one or more communication devices 120 across the communicationnetwork 112. This communication may include, but is in no way limitedto, transferring files, exchanging information, providing results in adigital format, causing the communication device 120 to render theresults of an analysis performed by the ADAQ system 104 of a subject,etc.

The elements dictionary 108 may be stored in a memory communicativelycoupled with the ADAQ system 104. In some embodiments, the elementsdictionary may be stored in a memory across the communication network112 (e.g., in the cloud, etc.). This storage can allow for an elementsdictionary 108 developed for a particular pathology, or type ofpathology, to be uploaded and/or stored remotely from a particular ADAQsystem 104. Continuing this example, the elements dictionary 108 may beaccessed and used by other ADAQ systems 104 in quantifying pathology inother subjects. For instance, an elements dictionary 108 may bedeveloped by an ADAQ system 104 in a particular geographical locationand uploaded, or stored, in a cloud memory. This elements dictionary 108may subsequently be used, accessed, or downloaded by another ADAQ system104 in different geographical location. Among other things, thisapproach allows multiple ADAQ systems, worldwide, to access theinformation (e.g., the elements dictionary, etc.) stored in the cloud.As can be appreciated, a diagnostic professional is provided instant ornear-instant access to the various stored elements dictionaries 108 forspecific pathologies. Using an ADAQ system 104 and the relevant elementsdictionary 108, the diagnostic professional may detect and quantifypathology of subjects anywhere the world.

In some embodiments, the elements dictionary 108 may be updated. Theupdate may include refining elements, providing additional descriptionfor element patterns, adding elements, and/or otherwise modifying thedata stored in the elements dictionary 108.

The communication network 112 may comprise any type of knowncommunication medium or collection of communication media and may useany type of protocols to transport messages between endpoints. Thecommunication network 112 may include wired and/or wirelesscommunication technologies. The Internet is an example of thecommunication network 112 that constitutes an Internet Protocol (IP)network consisting of many computers, computing networks, and othercommunication devices located all over the world, which are connectedthrough many telephone systems and other means. Other examples of thecommunication network 112 include, without limitation, a standard PlainOld Telephone System (POTS), an Integrated Services Digital Network(ISDN), the Public Switched Telephone Network (PSTN), a Local AreaNetwork (LAN), a Wide Area Network (WAN), a Voice over Internet Protocol(VoIP) network, a cellular network, and any other type ofpacket-switched or circuit-switched network known in the art. Inaddition, it can be appreciated that the communication network 112 neednot be limited to any one network type, and instead may be comprised ofa number of different networks and/or network types. The communicationnetwork 112 may comprise a number of different communication media suchas coaxial cable, copper cable/wire, fiber-optic cable, antennas fortransmitting/receiving wireless messages, and combinations thereof.

The image scan system 116 may include any device, machine, or systemthat is configured to scan an area or volume of a subject and generateimage data representative of that area or volume. An image scan system116 may be configured to generate image data of areas inside a subject(e.g., areas that cannot be observed, monitored, or detected fromoutside of the subject, etc.). In some embodiments, the image scansystem 116 may be a computed tomography (CT) system. For instance, theCT system may generate images of areas or regions inside a subject'sbody using x-rays taken in stepped increments or sections. Scan sectionsmay be taken along one or more cross-sections 122 of a subject body asillustrated by cross-section A-A. The scans may be made at fixedincrements (e.g., 0.5 mm, 1.0 mm, 2.0 mm, and so on). When the scans arecombined, a three-dimensional image may be generated of the inside of asubject volume, or portions thereof. Although discussed in conjunctionwith CT scanners and the like, it should be appreciated that the ADAQsystem may receive image data scans from any imaging or image scansystem in generating element dictionaries and/or quantifying pathologyin subject images. For instance, image data scans may be generated bymagnetic resonance imaging (MM) machines, X-ray machines, ultrasoundmachines, optical imaging modalities (e.g. microscope, etc) etc. Whiledescribed as using 2D imaging data, it should be appreciated thatembodiments of the present disclosure may similarly use 3D image data.In this case, the various regions/patches/blocks etc. may be an imagevolume (e.g., 28×28×28 voxels) or three 2D orthogonal image planes(e.g., each 28×28 pixels) that intersect at a point of interest.

The scanned image data memory 118 may be one or more disk drives,optical storage devices, solid-state storage devices such as a randomaccess memory (“RAM”) and/or a read-only memory (“ROM”), which can beprogrammable, flash-updateable and/or the like. In some embodiments,image scan data generated by an image scan system 116 may be stored inthe scanned image data memory 118. This image scan data may includetraining examples, subject examples, tagged examples, actual subjectscans, historical subject scans, and/or the like.

The communication devices 120 may correspond to at least one of a smartphone, tablet, personal computer, server, and/or some other computingdevice. Each communication device 120 may be configured with anoperating system (“OS”) and at least one communication application. Thecommunication application may be configured to exchange communicationsbetween the communication device 120 and another component (e.g., a ADAQsystem 104, elements dictionary memory 108, image scan system 116,scanned image data memory 118, another communication device 120, etc.)across the communication network 112. Additionally or alternatively,communications may be sent and/or received via the communication device120 as a call, a packet or collection of packets (e.g., IP packetstransmitted over an IP network), an email message, an instant message(“IM”), an SMS message, an MMS message, a video presentation, and/orcombinations thereof.

Referring to FIG. 2, a block diagram depicting an ADAQ system 104 isshown in accordance with embodiments of the present disclosure. The ADAQsystem 104 may include a processor 204 and a memory 208 that stores oneor more instruction sets, applications, or modules, potentially in theform of a feature extraction module 216, a region boundary module 220, apathology identification module 228, and/or a classification algorithm224 (e.g., a support vector machine, etc.). The ADAQ system 104 may beconfigured as a server, or part of a server, that includes one or moreof the components described in conjunction with FIG. 1. For instance,the ADAQ system 104 may include one or more elements dictionaries 108,scanned image data 118, and the like.

The processor 204 may correspond to one or more microprocessors that arecontained within a common housing, circuit board, or blade with thememory 208. The processor 204 may be a multipurpose, programmable devicethat accepts digital data as input, processes the digital data accordingto instructions stored in its internal memory, and provides results asoutput. The processor 204 may implement sequential digital logic as ithas internal memory. As with most microprocessors, the processor 204 mayoperate on numbers and symbols represented in the binary numeral system.

The memory 208 may correspond to any type of non-transitorycomputer-readable medium. In some embodiments, the memory 208 maycomprise volatile or non-volatile memory and a controller for the same.Non-limiting examples of memory 208 that may be utilized in the ADAQsystem 104 include RAM, ROM, buffer memory, flash memory, solid-statememory, or variants thereof. Any of these memory types may be considerednon-transitory computer memory devices even though the data storedthereby can be changed one or more times.

The user interface 212 may be configured as a graphical user interfaceor other interface by which a user can interact with the ADAQ system104. In some embodiments, results of an analysis performed by the ADAQsystem 104 may be rendered by the user interface 212. The user interface212 may be configured to display aspects of one or more of thecomponents of the ADAQ system 104. Additionally or alternatively, theuser interface 212 may be configured to present and/or render anapplication interface. The application interface may present a number ofuser interface elements to a user for one or more of training the system104, defining or generating new element dictionaries 108, detectingand/or quantifying pathology from image data scans, and more. In oneembodiment, the ADAQ system 104 may be configured in a client/serverarrangement. For example, a communication device 120 may include aclient application capable of interfacing with the ADAQ system 104server. This arrangement may allow for shared processing, multipleusers, a common server access, etc.

In some embodiments, the applications/instructions 216, 220, 224, 228may correspond to any type of computer-readable instructions or filesstorable in the memory 208. The feature extraction module 216 may beconfigured to analyze an image data scan (e.g., a CT scan, etc.) of asubject and extract image blocks from within a defined area of the imagedata scan. In one embodiment, the feature extraction module 216 mayutilize a fixed block size (e.g., 3.0 mm×3.0 mm) capture window movingwithin the defined area to extract image blocks. For instance, thecapture window may be moved within a defined area along a path orvector. As the capture window moves from one position to another, theimage falling inside the capture window at each defined position issaved to a memory, or extracted. This process continues until thecapture window has moved along all programmed positions within thedefined area.

The region boundary module 220 may be configured to automaticallydetermine the defined area in an image data scan for feature extraction,etc. This defined area may be based on anatomical physiognomy, imagingfiducials, characteristic patterns, boundary definition information,etc., and/or combinations thereof. It is anticipated that the regionboundary module 220 determines focused analysis areas for the ADAQsystem 104. This approach eliminates the need for the ADAQ system 104 toperform an analysis of all areas of the image data scan. In someembodiments, the region boundary module 220 may determine sub-regionswithin a focus region to further analyze. Sub-regions may be defined forcharacteristic sections of an image or known patterns of types ofpathology.

The classification algorithm 224 may be configured to generate thequantitative rules used in detecting pathology. The classificationalgorithm may include one or more of a support vector machine, logisticregression, random forest, nearest neighbors, etc. These machines and/oralgorithms may be used separately or in combination as an ensemble toform the classification algorithm 224. In some cases, the ADAQ system104 and/or the components of the ADAQ system 104 may include one morerules, conditions, predictive models, and/or methods generated bytraining the classification algorithm 224. For instance, theclassification algorithm 224 may be presented a number of trainingexamples. These training examples may include image data scans ofhealthy subjects, image data scans having pathology, and even image datascans identifying a particular pathology type. In some cases, thetraining of the classification algorithm 224 may include a diagnosticprofessional identifying regions of interest (e.g., those regions knowto have pathology, etc.) of a number of image data scans. These regionsof interest may be entered into the classifier algorithm 224 as aboundary within which the classification algorithm 224 (e.g., a supportvector machine, etc.) will determine its pathology classifiers. In anyevent, the classification algorithm 224 may communicate with theelements dictionary memory 108 to determine the elements that can beused in providing a quantitative classification. In other words, theelements dictionary includes the language, or known elements/features,that can be used in differentiating between healthy and unhealthytissue.

The pathology identification module 228 is configured to automaticallydetermine whether pathology exists in one or more regions of an imagedata scan or scans. The pathology identification module 228 may utilizethe elements in the element dictionary memory 108 to quantify regions ofa received image data scan. Once quantified, the pathologyidentification module 228 may determine whether the quantitative valuerepresents a specific pathology or pathology type. This determinationmay include viewing a plurality of regions and/or the quantitative valuerepresenting each region in the plurality of regions in a locality ofthe image data scan. The pathology identification module 228 may includequantitative rules for evaluating pathology, determining pathology type,identifying and/or marking areas, and even for generating results of theADAQ system 104. In some embodiments, the pathology identificationmodule 228 may communicate with the feature extraction module 216 ingenerating and/or updating the element dictionary 108

The network interface 232 may comprise hardware that facilitatescommunications with other elements, components, and/or devices over thecommunication network 112. The network interface 232 may include anEthernet port, a Wi-Fi card, a Network Interface Card (NIC), a cellularinterface (e.g., antenna, filters, and associated circuitry), or thelike. The network interface 232 may be configured to facilitate aconnection between the ADAQ system 104 and the communication network 112and may further be configured to encode and decode communications (e.g.,packets) according to a protocol utilized by the communication network112.

FIG. 3 is a block diagram depicting a method 300 of training anautomatic detection and quantification system and utilizing the trainedautomatic detection and quantification system in accordance withembodiments of the present disclosure. In training the machine learningalgorithm (e.g., a support vector machine, etc.), a number of trainingimages 303 may be presented to the ADAQ system 104. The training images303 may be labeled (e.g., as healthy, unhealthy, by pathology and/orpathology type, etc.) and areas of concern (ROIs) may be marked 309.Among other things, the ADAQ system 104 may extract features 212 fromthese training images 303 to define quantitative pathology determinationrules used in a predictive model. Upon receiving a new image 306, or animage data scan from a subject for diagnosis, the ADAQ system 104automatically extracts the features as described herein and applies thepredictive model to the extracted features or feature sets. An output ofthis application may be a label of whether a portion in the new imagedata scan includes healthy or unhealthy tissue regions. Areas ofconcern, or ROIs, in the new image region 306 may be automaticallyidentified and marked via the ADAQ system 104. In one embodiment, animage of the marked areas of concern, or ROIs, may be visuallyidentified by a highlight, mark, or other graphical report. This reportmay be rendered to the display of the ADAQ system 104 or at least onecommunication device 120.

FIG. 4 shows a detail view of an image block of a subject image datascan 400 in accordance with embodiments of the present disclosure. Inparticular, the subject image data scan 400 may include a regionboundary 404 and a defined feature area 408 within the region boundary404. As provided herein, the region boundary 404 may be determinedautomatically, or the region boundary 404 may be programmaticallyentered to focus or limit the feature extraction described herein. Inany event, FIG. 4 shows the defined feature area 408 includingapproximately 441 pixels (e.g., in a 21-by-21 pixel feature area 408).Each pixel may have a specific pixel intensity or luminance value forits location that quantitatively describes a level of brightness orlight intensity in a particular region. By way of example, the pixel inthe first row, first column (1, 1) at the top left of the detail featurearea 408 shows a luminance value of −945. Proceeding from left-to-right,the values read as follows: (1, 2)=−787; (1, 3)=−779; (1, 4)=−917; (1,5)=−941; (1, 6)=−913; (1, 7)=−547; and so on. In some embodiments, thedefined feature area 408 may define a specific pathology or type of apathology. In one embodiment, a cluster of the pixels may define aspecific pathology or type of a pathology. For instance, a capturewindow may be used to capture clusters of pixels that are adjacent toone another in the feature area 408. As provided herein, the capturewindow may include a fixed block size (e.g., in pixels or in physicaldimension such as inches or millimeters, etc.) to determinerelationships and characteristics of neighboring pixels. In determininga dictionary of elements to be used in the quantification of pathology,the information (e.g., luminance, histogram intensity, etc.) from eachcluster of pixels obtained by the capture window as it moves throughoutthe defined feature area 408 are stored in a memory and grouped.

FIG. 5 is a diagram illustrating a set of dictionary elements 508generated from one or more image data scans 504 in accordance withembodiments of the present disclosure. The dictionary elements 508 shownin FIG. 5 include only 64 basis elements for the sake of clarity. Itshould be appreciated, however, that the set of dictionary elements mayinclude any number of basis elements which can be used by the ADAQsystem 104 in identifying, classifying, and quantifying pathology. Insome embodiments, the overall approach described herein may be used in ahierarchical fashion to produce dictionaries of features at multiplescales. While embodiments disclosed herein describe classifying regions(which are described in terms of a dictionary of low-level features,etc.) using a trained SVM classifier, the present disclosure may includeproducing another dictionary at this scale instead—such that a seconddictionary of features at a somewhat larger scale is generated anddescribed in terms of the first dictionary at the smaller scale.

FIG. 6 is a diagram illustrating quantification of a subject region 604using the set of dictionary elements 508 generated in accordance withembodiments of the present disclosure. Once the set of dictionaryelements 508 is determined, a new image data scan 602 may be received bythe ADAQ system 104. In FIG. 6, a subject region 604 is quantified basedon, or using, the set of dictionary elements 508. The subject region 604may be quantified using the light intensity, luminance, and/or histogramcompared to the stored set of dictionary elements 508. By way ofexample, the subject region 604, or rather the regional/signatureluminance of the subject region 604, is equivalent to 0.56 of basiselement A 608A, plus 0.32 of basis element B 608B, plus 0.15 of basiselement C 608C. As the subject region is not identical to any one of thebasis elements in the set 508, each basis element used in calculatingthe signature for the subject region 604 luminance is adjusted by ascalar value 612A-C. This scalar adjustment allows for repeatable andaccurate quantification of particular regions based on standardizeddictionary elements for a pathology.

FIG. 7 shows a chart 700 of image data scans grouped by identifiedpathology types in accordance with embodiments of the presentdisclosure. For instance, the first row 704A may include various imagedata scans associated with reticular abnormalities in IPF. The secondrow 704B may include image data scans of subject tissue associated withhoneycombing in IPF. The third row 704C may include image data scans ofsubject tissue having traction bronchiectasis. As can be appreciated,each row may include one or more image data scans represented by thefirst, second, and third columns 708A-C. These image data scans may beused in training the ADAQ system 104 to predict pattern characteristicsassociated with various types of a pathology. In some cases, thecharacteristic patterns may be identified by an expert and delineated712A, 712B, 712C for feature extraction and analysis by the ADAQ system104.

FIGS. 8A-8B show image blocks 800A-B taken from image data scans used invector quantization in accordance with embodiments of the presentdisclosure. As shown in FIG. 8A a region of interest 804A may bedelineated by an irregular-shaped boundary. As part of a featureextraction process, the capture window 808A may move within the regionof interest 804A along a path defined by movement vector 812A, capturingand/or extracting features adjacent to one another (e.g., adjacent fixedsize capture windows, overlapping windows, etc.). Depending on thecontext, the features captured in this process may be used in trainingthe ADAQ system 104 or in identifying and quantifying pathology in animage data scan. FIGS. 8A-8B may correspond to “pooling,” where thedictionary may contain “low-level” features (e.g., in the 8×8 squarecapture window 808A, 808B) and regions to be classified are larger(804A, 804B). Average, maximum, and/or other methods for pooling,weighting coefficients for all overlapping capture blocks 808A, 808Bwithin the regions 804A, 804B can produce a feature vector for theirrespective region 804A, 804B that is then the input to the SVM or otherclassifier (e.g., classification algorithm 224, etc.).

FIG. 8B includes a region of interest 804B delineated by a squareboundary. In some embodiments, a region of interest 804B may comprise a30 by 30 pixel square. Similar to FIG. 8A, as part of a featureextraction process, the capture window 808B may move within the regionof interest 804B along one or more axes, capturing and/or extractingfeatures in adjacent to one another (e.g., adjacent fixed size capturewindows, overlapping windows, etc.). In some embodiments, the capturewindow 808B may comprise a square of 8 by 8 pixels. Depending on thecontext, the features captured in this process may be used in trainingthe ADAQ system 104 or identifying and quantifying pathology in an imagedata scan. In some embodiments, the square region may be taken fromwithin the irregular-shaped boundary described in FIG. 8A. Samplingirregularly shaped regions (as shown in FIG. 8A) in new images may beperformed with an image processing method called “super-pixels.” Usingthis method may allow for an efficient sampling of new images. Forexample, simply sampling or extracting square regions may result inincomplete or mixed patterns within the region since natural images tendnot to follow regular square shaped blocks.

FIG. 9A is a diagram of an image data scan 900A including humanidentified regions of interest. As shown in the image data scan 900A ofFIG. 9A, an expert or diagnostic professional may determine one or moreregions of interest 901A-I by outlining or otherwise marking areas inthe scan. However, as there are many image data scans and interpretationbias between experts (e.g., inter-observer variations, etc.) the expertmay not accurately determine all pathology or relevant regions ofinterest.

FIG. 9B is a diagram of an image data scan 900B including identifiedregions of interest by the ADAQ system 104 in accordance withembodiments of the present disclosure. The quantitative analysisperformed by the ADAQ system 104 removes any variation or bias and maydetermine regions of interest that escape the attention of an expert.For example, FIG. 9B includes a first area 904 and a second area 908that went unidentified by a human expert (e.g., described and shown inconjunction with FIG. 9A).

FIG. 10A shows a sample image data scan 1000A of a subject in accordancewith embodiments of the present disclosure. As provided above, the imagedata scan 1000A may be a CT scan of a section of a subject lungs. In thesample image data scan 1000A of FIG. 10A no pathology may be visuallydetected by an expert. However, pathology may be evident to the ADAQsystem 104, which can detect characteristic patterns based on thedictionary of elements generated for a pathology. Because the ADAQsystem 104 uses feature extraction for elements on a pixel clusterbases, the ADAQ system 104 can detect pathology that is undetectable bythe human eye. For example, FIG. 10B shows the image data scan of FIG.10A including at least three human-undetectable regions of interest1004, 1008, 1012 that can be detected and identified by the ADAQ system104 in accordance with embodiments of the present disclosure.

FIG. 11 is a flow chart depicting a method 1100 of generating adictionary of elements in accordance with embodiments of the presentdisclosure. While a general order for the steps of the method 1100 isshown in FIG. 11, the method 1100 can include more or fewer steps or canarrange the order of the steps differently than those shown in FIG. 11.Generally, the method 1100 starts with a start operation 1104 and endswith an end operation 1128. The method 1100 can be executed as a set ofcomputer-executable instructions executed by a computer system andencoded or stored on a computer readable medium. Hereinafter, the method1100 shall be explained with reference to the systems, components,modules, software, data structures, user interfaces, etc. described inconjunction with FIGS. 1-10.

The method 1100 begins at 1104 and proceeds by receiving image datascans (step 1108). These image data scans may be received by the ADAQsystem 104. As provided above, the image data scans may be generated byone or more image scan systems 116 and may even be associated with anumber of different subjects. In some embodiments, the image data scansrepresent a digital image of a cross-section of at least one area orvolume of a subject (e.g., a patient, animal, etc.). The image datascans may be sent by the image scan system 116 across a communicationnetwork 112 with which the ADAQ system 104 is communicatively connected.In some embodiments, the ADAQ system 104 may retrieve the images from adatabase, a scanned image data memory 118, an image scan system 116,and/or some other memory.

The method 1100 continues by extracting features and/or image blocksfrom each of the image data scans (step 1112). In some embodiments, thefeature extraction may include defining a fixed block size for datacollection. In other words, the fixed block size may define the size(e.g., area, length, width, dimension etc.) of each basis element in thedictionary of elements. Images may be volumetric (3D) and/or containmulti-modal information (i.e., combinations of various imagingmodalities to create a composite image). The fixed block size maycorrespond to a capture window size that is used to collect informationfrom the image data scan or a region thereof. For example, as thecapture window moves along a path and/or within a delineated area of theimage data scan, the capture window can store features as image blocksin one or more memories associated with the ADAQ system 104. In someembodiments, a pre-processing step may include standardizing blocks(e.g., so mean pixel value is zero and standard deviation is 1.0, etc.).Additionally or alternatively, data “whitening” or other type ofmathematical filtering may be used.

Next, the method 1100 proceeds by applying vector quantization usingpixel intensities in each image block captured/extracted (step 1116). Inthis step, the ADAQ system 104 may divide the collection of differentpixel clusters having specific intensities and locations into specificgroups defining recurring patterns. These patterns may serve to indicatevarious states of tissue in an image scan including, but in no waylimited to, healthy, unhealthy, deteriorating, and/or variationsthereof.

Using the grouped information, the method 1100 continues by generating adictionary of elements describing the patterns of extracted features(step 1120). Generating the dictionary may include a set of basiselements that represent the patterns determined through vectorquantization, etc. The basis elements may later be used to quantifypathology and/or tissue in image data scans. It is anticipated that adictionary of elements may be generated for specific regions of asubject, specific pathologies, specific types of a particular pathology,etc., and/or combinations thereof. In some cases, the dictionary ofelements may be further refined by repeating the method 1100 usingadditionally received image data scans. In some embodiments,dictionaries may also be refined using other methods, possibly featureselection methods to optimize number of elements in a dictionary orother methods to update dictionary elements to maximize classificationaccuracy. Additionally or alternatively, the methods, devices, andsystems described herein may be employed to generate multipledictionaries at different scales—“layers” of dictionaries describinghierarchical features, etc. The dictionary of elements may include afixed number of basis elements for specific quantization of pathology.

Once generated, the dictionary of elements may be stored in a memory(step 1124). This memory may be associated with the ADAQ system 104 andmay be stored remotely from and/or locally with the ADAQ system. In oneembodiment, the dictionary of elements may be stored in the cloudaccessible via a communication network 112. In any event, the method1100 ends at step 1128.

FIG. 12 is a flow chart depicting a method 1200 of training an ADAQsystem 104 in accordance with embodiments of the present disclosure.While a general order for the steps of the method 1200 is shown in FIG.12, the method 1200 can include more or fewer steps or can arrange theorder of the steps differently than those shown in FIG. 12. Generally,the method 1200 starts with a start operation 1204 and ends with an endoperation 1228. The method 1200 can be executed as a set ofcomputer-executable instructions executed by a computer system andencoded or stored on a computer readable medium. Hereinafter, the method1200 shall be explained with reference to the systems, components,modules, software, data structures, user interfaces, etc. described inconjunction with FIGS. 1-11.

The method 1200 begins at step 1204 and proceeds by delineating regionsof interest in select image data scans (step 1208). In some embodiments,the select image data scans may be taken from the image data scans usedin generating the dictionary of elements described in conjunction withFIG. 11. The delineation may be performed by a diagnostic professionalor an expert. In some cases, the delineation is used to define regionsof interest in the image data scans having pathology or a specific typeof a pathology. Delineation may include the expert outlining theseregions of interest using a digitizing pen and surface (e.g., tablet,screen, etc.). By way of example, an image data scan having pathologymay be displayed to a user interface display device. The expert maydetermine the image includes a region of pathology. Then, the expert mayuse the digitizing pen to outline, or highlight, the region of interest.The delineated image or region may then be saved to memory.

Next, the delineated image may be associated with a particular pathologyand/or a label (step 1212). For instance, the expert may have identifiedthe pathology delineated as IPF in the image data scan. Additionally oralternatively, the expert may determine that the delineated regionincludes a particular manifestation, or type, of IPF such ashoneycombing. In any event, the delineated image may be saved with alabel, grouped, or otherwise identified with the particular pathologyand/or type of particular pathology. In some embodiments, this label maybe read by the ADAQ system 104 upon reviewing the delineated region,related basis elements in the dictionary of elements, and/or thedelineated image data scan. The delineated regions and their labels maybe stored in memory serving to capture the expert work product.

In some cases, the method 1200 may continue by verifying the delineatedregions of interest and any associated labels/identifications (step1216). This verification may be performed by a second different expertand/or diagnostic professional. The verification may include a blindstudy of the image data scans where the second expert is required todelineate regions of interest in unmarked image data scans. In oneembodiment, the verification may include the second expert reviewing themarked or delineated image data scans for accuracy. Any differences arenoted and saved with the delineated image data scans.

Next, the delineated regions of interest and accompanying associationlabels are presented to the ADAQ system 104 (e.g., the classificationalgorithm 224, etc.) for training (1220). In training the ADAQ system104, the dictionary of elements, healthy image data scans, unhealthyimage data scans (e.g., the delineated image data scans havingpathology, etc.) are provided to the support vector machine or otherclassification algorithm 224 (e.g., as training examples) to determine aclassification model for use in quantifying pathology in any image datascan. The classification model is capable of performing an analysis ofan image data scan and determine a quantified value representing whetherand to what extent the image data scan includes pathology markers.

The method 1200 continues by storing the trained classification system,or classification model, in a memory associated with the ADAQ system 104(step 1224). This memory may be associated with the ADAQ system 104 andmay be stored remotely from and/or locally with the ADAQ system 104. Inone embodiment, the classification model may be stored in the cloud andbe accessible across a communication network 112. In any event, themethod 1200 ends at step 1228.

FIG. 13 is a flow chart depicting a method 1300 of identifying pathologyin the automatic detection and quantification of pathology in accordancewith embodiments of the present disclosure. While a general order forthe steps of the method 1300 is shown in FIG. 13, the method 1300 caninclude more or fewer steps or can arrange the order of the stepsdifferently than those shown in FIG. 13. Generally, the method 1300starts with a start operation 1304 and ends with an end operation 1332.The method 1300 can be executed as a set of computer-executableinstructions executed by a computer system and encoded or stored on acomputer readable medium. Hereinafter, the method 1300 shall beexplained with reference to the systems, components, modules, software,data structures, user interfaces, etc. described in conjunction withFIGS. 1-12.

The automatic detection and quantification of pathology method 1300begins at step 1304 and proceeds by receiving image data scans for asubject (step 1308). In some embodiments, these image data scans may beassociated with a single subject, or individual, to determine whetherany pathology in the scans exist. The image data scans may be generatedby one or more image scan systems 116. As provided above, the image datascans represent a digital image of a cross-section of at least one areaor volume of a subject (e.g., a patient, animal, etc.). The image datascans may be sent by the image scan system 116 across a communicationnetwork 112 with which the ADAQ system 104 is communicatively connected.In some embodiments, the ADAQ system 104 may retrieve the images from adatabase, a scanned image data memory 118, an image scan system 116,and/or some other memory.

Next, the method 1300 continues by extracting features and/or imageblocks from each of the image data scans (step 1312). In someembodiments, the feature extraction may include defining a fixed blocksize for data collection. In other words, the fixed block size maydefine the size (e.g., area, length, width, etc.) that corresponds tothe size used in generating each of the basis elements in the dictionaryof elements. Similarly, the fixed block size may correspond to a capturewindow size that is used to collect information from the image data scanor a region thereof. For example, as the capture window moves along apath and/or within the image data scan, the capture window can storefeatures as image blocks in one or more memories associated with theADAQ system 104.

The method 1300 proceeds by determining whether any areas of tissueillustrated in the image data scans received include pathology (step1316). In some embodiments, the ADAQ system 104 may apply theclassification model described in conjunction with FIG. 12, to thecollected information and extracted feature blocks from the image datascans received in step 1308 (step 1316). The determination may includeclassifying each feature block as a quantified value using the set ofbasis elements in the element dictionary. This classification isdescribed in greater detail in conjunction with FIGS. 5 and 6. Forexample, each feature block may be valued as at least one basis elementmultiplied by a scalar value. Similar to the values determined for thedictionary of elements, the extracted feature blocks are classified bypixel intensity and feature similarity to basis elements in thedictionary.

Any tissue determined to have pathology, via the automatic determinationdescribed in step 1316, is marked with an identifier (step 1320). Eachidentifier may be associated with a row and column position in aparticular image data scan. In some cases, the identifier may define aregion or boundary around a group of pixels in an image data scan. Theregion may be defined by a measurement value associated with the scan.In some embodiments, the region or boundary may be defined by pixelposition in the image data scan. In any event, the identifier isassociated with a particular image data scan.

Next, the method 1300 stores the image data scan including theidentification marking (step 1324). This may include storing a newversion or file of the image data scan that includes markings ofpathology. The marked image data scans may be stored in any memorydescribed herein.

In some embodiments, the method 1300 may proceed by rendering theidentifier of pathology to a display device (step 1328). As providedabove, the identifier may provided on a modified image data scan. Forinstance, the identifier may highlight the location of any detectedpathology. In some embodiments, the identifier may classify thepathology type. As can be appreciated, this marking may be rendered tothe display of a communication device 120, the ADAQ system 104, theimage scan system 116, or any other device communicatively coupled tothe ADAQ system 104. The marking may utilize one or more color, shape,size, hatching, area fill, shading, line type, line thickness, and/orother visual application to illustrate the pathology location and/ortype. In some embodiments, a report may be generated for the resultsdetermined. For example, the report may be include a score, which in theIPF example may be fibrosis score based on fraction of regions testedthat are classified as fibrosis (e.g., score=(# regions class.fibrotic)/(total # regions tested), etc.).

In some cases, the identification of pathology may be accompanied by areport. The report may provide a quantification of the amount ofpathology in a subject, in a region, and/or in a scan. Moreover, thereport may summarize an overall stage of the pathology. In any event,the report may be rendered to the display device. It is an aspect of thepresent disclosure that an alarm is provided when pathology is detectedabove a threshold value. In some cases, the alarm may indicate thattreatment of the pathology, if available or possible, is associated witha particular level of urgency. The alarm and/or any levels,descriptions, or accompanying information may be rendered via thedisplay device. The method 1300 ends at step 1332.

The exemplary systems and methods of this disclosure have been describedin relation to computers, imaging devices, diagnostic devices, systems,and methods in a pathology detection system. However, to avoidunnecessarily obscuring the present disclosure, the precedingdescription omits a number of known structures and devices. Thisomission is not to be construed as a limitation of the scopes of theclaims. Specific details are set forth to provide an understanding ofthe present disclosure. It should, however, be appreciated that thepresent disclosure may be practiced in a variety of ways beyond thespecific detail set forth herein. Moreover, it should be appreciatedthat the methods disclosed herein may be executed via a wearable device,a mobile device, a reading device, a communication device, and/or anaccess server of an access control system, etc.

Furthermore, while the exemplary aspects, embodiments, options, and/orconfigurations illustrated herein show the various components of thesystem collocated, certain components of the system can be locatedremotely, at distant portions of a distributed network, such as a LANand/or the Internet, or within a dedicated system. Thus, it should beappreciated, that the components of the system can be combined in to oneor more devices, such as a Personal Computer (PC), laptop, netbook,smart phone, Personal Digital Assistant (PDA), tablet, etc., orcollocated on a particular node of a distributed network, such as ananalog and/or digital telecommunications network, a packet-switchnetwork, or a circuit-switched network. It will be appreciated from thepreceding description, and for reasons of computational efficiency, thatthe components of the system can be arranged at any location within adistributed network of components without affecting the operation of thesystem. For example, the various components can be located in a switchsuch as a PBX and media server, gateway, in one or more communicationsdevices, at one or more users' premises, or some combination thereof.Similarly, one or more functional portions of the system could bedistributed between a telecommunications device(s) and an associatedcomputing device.

Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information. Transmission media usedas links, for example, can be any suitable carrier for electricalsignals, including coaxial cables, copper wire and fiber optics, and maytake the form of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications.

Also, while the flowcharts have been discussed and illustrated inrelation to a particular sequence of events, it should be appreciatedthat changes, additions, and omissions to this sequence can occurwithout materially affecting the operation of the disclosed embodiments,configuration, and aspects.

A number of variations and modifications of the disclosure can be used.It would be possible to provide for some features of the disclosurewithout providing others.

Optionally, the systems and methods of this disclosure can beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, a hard-wired electronic or logic circuit such as discreteelement circuit, a programmable logic device or gate array such as PLD,PLA, FPGA, PAL, special purpose computer, any comparable means, or thelike. In general, any device(s) or means capable of implementing themethodology illustrated herein can be used to implement the variousaspects of this disclosure. Exemplary hardware that can be used for thedisclosed embodiments, configurations and aspects includes computers,handheld devices, telephones (e.g., cellular, Internet enabled, digital,analog, hybrids, and others), and other hardware known in the art. Someof these devices include processors (e.g., a single or multiplemicroprocessors), memory, nonvolatile storage, input devices, and outputdevices. Furthermore, alternative software implementations including,but not limited to, distributed processing or component/objectdistributed processing, parallel processing, or virtual machineprocessing can also be constructed to implement the methods describedherein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis disclosure is dependent on the speed and/or efficiency requirementsof the system, the particular function, and the particular software orhardware systems or microprocessor or microcomputer systems beingutilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this disclosurecan be implemented as program embedded on personal computer such as anapplet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Although the present disclosure describes components and functionsimplemented in the aspects, embodiments, and/or configurations withreference to particular standards and protocols, the aspects,embodiments, and/or configurations are not limited to such standards andprotocols. Other similar standards and protocols not mentioned hereinare in existence and are considered to be included in the presentdisclosure. Moreover, the standards and protocols mentioned herein andother similar standards and protocols not mentioned herein areperiodically superseded by faster or more effective equivalents havingessentially the same functions. Such replacement standards and protocolshaving the same functions are considered equivalents included in thepresent disclosure.

The present disclosure, in various aspects, embodiments, and/orconfigurations, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious aspects, embodiments, configurations embodiments,subcombinations, and/or subsets thereof. Those of skill in the art willunderstand how to make and use the disclosed aspects, embodiments,and/or configurations after understanding the present disclosure. Thepresent disclosure, in various aspects, embodiments, and/orconfigurations, includes providing devices and processes in the absenceof items not depicted and/or described herein or in various aspects,embodiments, and/or configurations hereof, including in the absence ofsuch items as may have been used in previous devices or processes, e.g.,for improving performance, achieving ease and/or reducing cost ofimplementation.

The foregoing discussion has been presented for purposes of illustrationand description. The foregoing is not intended to limit the disclosureto the form or forms disclosed herein. In the foregoing DetailedDescription for example, various features of the disclosure are groupedtogether in one or more aspects, embodiments, and/or configurations forthe purpose of streamlining the disclosure. The features of the aspects,embodiments, and/or configurations of the disclosure may be combined inalternate aspects, embodiments, and/or configurations other than thosediscussed above. This method of disclosure is not to be interpreted asreflecting an intention that the claims require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive aspects lie in less than all features of a singleforegoing disclosed aspect, embodiment, and/or configuration. Thus, thefollowing claims are hereby incorporated into this Detailed Description,with each claim standing on its own as a separate preferred embodimentof the disclosure.

Moreover, though the description has included description of one or moreaspects, embodiments, and/or configurations and certain variations andmodifications, other variations, combinations, and modifications arewithin the scope of the disclosure, e.g., as may be within the skill andknowledge of those in the art, after understanding the presentdisclosure. It is intended to obtain rights which include alternativeaspects, embodiments, and/or configurations to the extent permitted,including alternate, interchangeable and/or equivalent structures,functions, ranges or steps to those claimed, whether or not suchalternate, interchangeable and/or equivalent structures, functions,ranges or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

Any of the steps, functions, and operations discussed herein can beperformed continuously and automatically.

Examples of the processors as described herein may include, but are notlimited to, at least one of Qualcomm® Snapdragon® 800 and 801, Qualcomm®Snapdragon® 610 and 615 with 4G LTE Integration and 64-bit computing,Apple® A7 processor with 64-bit architecture, Apple® M7 motioncoprocessors, Samsung® Exynos® series, the Intel® Core™ family ofprocessors, the Intel® Xeon® family of processors, the Intel® Atom™family of processors, the Intel Itanium® family of processors, Intel®Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nmIvy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300,and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments®Jacinto C6000™ automotive infotainment processors, Texas Instruments®OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors,ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalentprocessors, and may perform computational functions using any known orfuture-developed standard, instruction set, libraries, and/orarchitecture. In some embodiments, functions may be performed by one ormore graphics processing units (GPUs). Such GPUs may include, but arenot limited to, at least one of an Nvidia Titan Xp or similar (GTX 1080Ti, GTX 1070, GTX 1080, etc.).

What is claimed is:
 1. An automatic detection and quantification system,comprising: a server, comprising: a processor; and a computer readablemedium, coupled to the microprocessor and comprising instructions storedthereon that cause the processor to: receive an input image data scan ofa subject; extract features from the input image data scan; determine,using an elements dictionary and a classification model, areas of normaland/or abnormal pathology of the input image data scan; generate amarked image data scan, comprising marking the determined pathologyareas with a visual identifier on the input image data scan; compute oneor more quantitative scores based on a comparison of a number of regionsclassified as various categories of normal and/or abnormal and a numberof regions tested; and store the marked image data scan and the one ormore quantitative scores in a memory.
 2. The automatic detection andquantification system of claim 1, wherein the elements dictionary isgenerated based on one or more sample image data scans received fromdifferent subjects.
 3. The automatic detection and quantification systemof claim 2, wherein the one or more sample image data scans comprisehealthy image data scans and pathology image data scans.
 4. Theautomatic detection and quantification system of claim 3, wherein theclassification model is generated in an unsupervised fashion using theelements dictionary and an additional clustering process.
 5. Theautomatic detection and quantification system of claim 4, wherein thetraining examples comprise delineated image data scans outlining regionsof interest having pathology.
 6. The automatic detection andquantification system of claim 5, further comprising: an image scansystem configured to provide cross-sectional image data scans of asubject, and wherein the server is communicatively coupled to the imagescan system.
 7. The automatic detection and quantification system ofclaim 6, wherein the marked image data scan is transmitted to acommunication device across a communication network.
 8. The automaticdetection and quantification system of claim 7, wherein the marked imagedata scan is configured to be rendered by a display device of thecommunication device.
 9. The automatic detection and quantificationsystem of claim 8, wherein the marked image data scan and identifierdescribes a pathology type and quantification value associated with thepathology areas.
 10. A method of generating a dictionary of elements foruse in an automatic detection and quantification of pathology,comprising: receiving, by a processor, a plurality of image data scansrepresenting cross-sectional areas of portions of one or more subjects;extracting, by the processor, features from a region in each of theplurality of image data scans; applying, via the processor, vectorquantization to the extracted features for grouping similar features;determining, via the processor, a set of basis elements for thedictionary of elements, wherein each of the set of basis elementsrepresents a group of the similar features; storing the dictionary ofelements in a memory.
 11. The method of claim 10, wherein extracting thefeatures comprises: determining, via the processor, a fixed block sizehaving a specific pixel area and representing a basis element size;moving, via the processor, a capture window of the fixed block sizealong a path and at specific points within the region in each of theplurality of image data scans; storing, via the processor, an imagecaptured at each of the specific points along the path in a memory. 12.The automatic detection and quantification system of claim 1, whereinmultiple elements dictionaries are generated based on sample image datascans received from different subjects.
 13. The automatic detection andquantification system of claim 12, wherein the multiple elementsdictionaries comprise hierarchical dictionaries for features at multiplescales.
 14. The automatic detection and quantification system of claim12, wherein the multiple elements dictionaries comprise separatedictionaries for normal features and abnormal features.
 15. A computerprogram product, comprising: a non-transitory computer readable storagemedium having computer readable program code embodied therewith, thecomputer readable program code comprising: instructions to receive, byan image scanner, an input image data scan of a subject; instructions toextract, by a processor, features from the input image data scan;instructions to determine, by the processor, using an elementsdictionary and a classification model, pathology areas of the inputimage data scan; instructions to generate, by the processor, a markedimage data scan, comprising marking the determined pathology areas witha visual identifier on the input image data scan; instructions tocompute, by the processor, one or more quantitative scores based on acomparison of a number of regions classified as abnormal and a number ofregions tested; and instructions to store, by the processor, the markedimage data scan and the one or more quantitative scores in a memory. 16.The computer program product of claim 15, wherein the elementsdictionary is generated based on one or more sample image data scansreceived from different subjects.
 17. The computer program product ofclaim 15, wherein the one or more sample image data scans comprisehealthy image data scans and pathology image data scans.
 18. Thecomputer program product of claim 17, wherein the classification modelis generated using training examples and the elements dictionary. 19.The computer program product of claim 18, wherein the training examplescomprise delineated image data scans outlining regions of interesthaving pathology.
 20. The computer program product of claim 19, furthercomprising: an image scan system configured to provide cross-sectionalimage data scans of a subject, and wherein the server is communicativelycoupled to the image scan system.